The COVID-19 Pandemic and In-Person Visit Rate Disruptions Among Patients With Hematologic Neoplasms in the US in 2020 to 2021.

Importance: The COVID-19 pandemic has led to a reduction in routine in-person medical care; however, it is unknown whether there have been any changes in visit rates among patients with hematologic neoplasms. Objective: To examine associations between the COVID-19 pandemic and in-person visits and telemedicine use among patients undergoing active treatment for hematologic neoplasms. Design, Setting, and Participants: Data for this retrospective observational cohort study were obtained from a nationwide electronic health record-derived, deidentified database. Data for patients with hematologic neoplasms who had received at least 1 systemic line of therapy between March 1, 2016, and February 28, 2021, were included. Treatments were categorized into 3 types: oral therapy, outpatient infusions, and inpatient infusions. The data cutoff date was April 30, 2021, when study analyses were conducted. Main Outcomes and Measures: Monthly visit rates were calculated as the number of documented visits (telemedicine or in-person) per active patient per 30-day period. We used time-series forecasting methods on prepandemic data (March 2016 to February 2020) to estimate expected rates between March 1, 2020, and February 28, 2021 (if the pandemic had not occurred). Results: This study included data for 24 261 patients, with a median age of 68 years (IQR, 60-75 years). A total of 6737 patients received oral therapy, 15 314 received outpatient infusions, and 8316 received inpatient infusions. More than half of patients were men (14 370 [58%]) and non-Hispanic White (16 309 [66%]). Early pandemic months (March to May 2020) demonstrated a significant 21% reduction (95% prediction interval [PI], 12%-27%) in in-person visit rates averaged across oral therapy and outpatient infusions. Reductions in in-person visit rates were also significant for all treatment types for multiple myeloma (oral therapy: 29% reduction; 95% PI, 21%-36%; P = .001; outpatient infusions: 11% reduction; 95% PI, 4%-17%; P = .002; inpatient infusions: 55% reduction; 95% PI, 27%-67%; P = .005), for oral therapy for chronic lymphocytic leukemia (28% reduction; 95% PI, 12%-39%; P = .003), and for outpatient infusions for mantle cell lymphoma (38% reduction; 95% PI, 6%-54%; P = .003) and chronic lymphocytic leukemia (20% reduction; 95% PI, 6%-31%; P = .002). Telemedicine visit rates were highest for patients receiving oral therapy, with greater use in the early pandemic months and a subsequent decrease in later months. Conclusions and Relevance: In this cohort study of patients with hematologic neoplasms, documented in-person visit rates for those receiving oral therapy and outpatient infusions significantly decreased during the early pandemic months but returned to close to projected rates in the later half of 2020. There were no statistically significant reductions in the overall in-person visit rate for patients receiving inpatient infusions. There was higher telemedicine use in the early pandemic months, followed by a decline, but use was persistent in the later half of 2020. Further studies are needed to ascertain associations between the COVID-19 pandemic and subsequent cancer outcomes and the evolution of telemedicine use for care delivery.

Ophthalmologist Turnover in the United States: Analysis of Workforce Changes from 2014 through 2021.

PURPOSE: Physician turnover is costly to health care systems and can affect patient experience due to discontinuity of care. This study aimed to assess the frequency of turnover by ophthalmologists and characteristics associated with turnover. DESIGN: A retrospective cross-sectional study. PARTICIPANTS: Actively practicing US ophthalmologists included in the Centers for Medicare & Medicaid Services (CMS) Physician Compare and Physician and Other Supplier Public Use File between 2014 and 2021. METHODS: Using two separate publicly available Medicare data sets, we collated data for ophthalmologists associated with practices in each year between 2014 and 2021. We calculated the rate of turnover as (1) annually in each year window and (2) cumulatively as the total proportion of 2014 practices separated by 2021. Multivariate logistic regression analysis was used to identify physician and practice characteristics associated with cumulative turnover. Additionally, we evaluated changes in annual turnover surrounding the Coronavirus disease 2019 pandemic. MAIN OUTCOME MEASURES: Ophthalmologist turnover, defined as a change of an ophthalmologist's National Provider Identifier practice affiliation from one year to the next. RESULTS: Of 13,264 ophthalmologists affiliated with 3,306 unique practices, 34.1% separated from at least one practice between 2014 and 2021. Annual turnover ranged from 3.7% (2017) to 19.4% (2018), with an average rate of 9.4%. Factors associated with increased turnover included solo practice (adjusted odds ratio [aOR], 9.59, p<0.01), university-affiliation (aOR, 1.55, p<0.01), practice location in the Northeast (aOR 1.39, p<0.01), and practice size of 2-4 members (aOR, 1.21, p<0.01). Factors associated with decreased turnover included male gender (aOR, 0.87, p<0.01), and greater than 5 years of practice: 6-10 years (aOR, 0.63), 11-19 years (aOR, 0.54), 20-29 years (aOR, 0.36), and ≥30 years (aOR, 0.18) (p < 0.01 for all). In the initial year of the COVID-19 pandemic (2020), annual turnover grew from 7.8% to 11.0%, then fell to 8.7% in the pandemic post-vaccine period (2021). CONCLUSIONS: One-third of US ophthalmologists separated from at least one practice from 2014-2021. Turnover patterns differ by various physician and practice characteristics, the knowledge of which may prove useful when developing strategies to optimize future workforce stability. Because reasons for turnover cannot be solely determined using administrative data, further investigation is warranted given the potential clinical and financial implications.

Private equity in ophthalmology and optometry: a time series analysis from 2012 to 2021.

Purpose: To identify temporal and geographic trends in private equity (PE)-backed acquisitions of ophthalmology and optometry practices in the United States from 2012 to 2021. Methods: In this cross-sectional time series, acquisition data from 10/21/2019 to 9/1/2021 and previously published data from 1/1/2012 to 10/20/2019 were analyzed. Acquisition data were compiled from 6 financial databases, 5 industry news outlets, and publicly available press releases. Linear regression models were used to compare rates of acquisition. Outcomes included number of total acquisitions, practice type, locations, provider details, and geographic footprint. Results: A total of 245 practices associated with 614 clinical locations and 948 ophthalmologists or optometrists were acquired by 30 PE-backed platform companies between 10/21/2019 and 9/1/2021. Of 30 platform companies, 18 were new vis-à-vis our prior study. Of these acquisitions, 127 were comprehensive practices, 29 were retina practices, and 89 were optometry practices. From 2012 to 2021, monthly acquisitions increased by 0.947 acquisitions per year (P < 0.001*). Texas, Florida, Michigan, and New Jersey were the states with the greatest number of PE acquisitions, with 55, 48, 29, and 28 clinic acquisitions, respectively. Average monthly PE acquisitions were 5.71 per month from 1/1/2019 to 2/29/2020 (pre-COVID), 5.30 per month from 3/1/2020 to 12/31/2020 (COVID pre-vaccine [P = 0.81]), and 8.78 per month from 1/1/2021 to 9/1/2021 (COVID post-vaccine [P = 0.20]). Conclusions: PE acquisitions increased during the period 2012-2021 as companies continue to utilize regionally focused strategies for acquisitions.

Follow-up Rates After Teleretinal Screening for Diabetic Retinopathy: Assessing Patient Barriers to Care.

Purpose: To study patient follow-up after they engage in a teleretinal screening program and to understand potential barriers to care. Methods: This was a retrospective analysis and a prospective study of telephone-based patient interviews of outpatients screened for diabetic retinopathy (DR) through a teleretinal referral system. Results: Of 2761 patients screened through a teleretinal referral program, 123 (4.5%) had moderate nonproliferative DR (NPDR), 83 (3.0%) had severe NPDR, and 31 (1.1%) had proliferative DR. Of the 114 patients with severe NPDR or worse, 67 (58.8%) saw an ophthalmologist within 3 months of referral. Eighty percent of interviewed patients reported they were not aware of the need for follow-up eye appointments. Conclusions: Of patients with severe retinopathy or worse, 58.8% presented for in-person evaluation and treatment within 3 months of screening. Although this result was negatively affected by factors related to the COVID-19 pandemic, key elements of patient education and improved referral strategies to facilitate in-person treatment are essential to improving follow-up after patients engage in telescreening.

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.

Trends in Enrollment in Employer-Sponsored Health Insurance in the US Before and During the COVID-19 Pandemic, January 2019 to June 2021.

This cross-sectional study compares trends in employer-sponsored health insurance coverage in the US before and during the COVID-19 pandemic.

Digital Health Applications in Oncology: An Opportunity to Seize.

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.

Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial.

INTRODUCTION: Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status. METHODS AND ANALYSIS: This single-centre prospective randomised controlled trial randomises patients with metastatic gastrointestinal or lung cancers into one of three arms: (A) usual care, (B) an intervention that integrates PGHD (including weekly text-based symptom surveys and passively recorded step counts) into a dashboard delivered to oncology clinicians at each visit and (C) the same intervention as arm B but with an additional text-based active choice intervention to patients to encourage discussing their symptoms with their oncology team. The study will enrol approximately 125 participants. The coprimary outcomes are patient perceptions of their oncology team's understanding of their symptoms and their functional status. Secondary outcomes are intervention utility and adherence. ETHICS AND DISSEMINATION: This study has been approved by the institutional review board at the University of Pennsylvania. Study results will be disseminated using methods that describe the results in ways that key stakeholders can best understand and implement. TRIAL REGISTRATION NUMBERS: NCT04616768 and 843 616.

Impact of Early COVID-19 Pandemic on Common Ophthalmic Procedures Volumes: A US Claims-Based Analysis.

PURPOSE: The COVID-19 pandemic has had a profound effect on the delivery of healthcare in the United States and globally. The aim of this study was to evaluate the impact of COVID-19 on common ophthalmic procedure utilization and normalization to pre-pandemic daily rates. METHODS: Leveraging a national database, Clinformatics™ DataMart (OptumInsight, Eden Prairie, MN), procedure frequencies and daily averages, defined by Current Procedural Terminology codes, of common elective and non-elective procedures within multiple ophthalmology sub-specialties were calculated. Interrupted time-series analysis with a Poisson regression model and smooth spline functions was used to model trends in pre-COVID-19 (January 1, 2018-February 29, 2020) and COVID-19 (March 1, 2020-June 30, 2020) periods. RESULTS: Of 3,583,231 procedures in the study period, 339,607 occurred during the early COVID-19 time period. Anti-vascular endothelial growth factor injections (44,412 to 39,774, RR 1.01, CI 0.99-1.02; p = .212), retinal detachment repairs (1,290 to 1,086, RR 1.07, CI 0.99-1.15; p = .103), and glaucoma drainage implants/trabeculectomies (706 to 487, RR 0.93, CI 0.83-1.04; p = .200) remained stable. Cataract surgery (61,421 to 33,054, RR 0.77; CI 0.76-0.78; p < .001), laser peripheral iridotomy (1,875 to 890, RR 0.82, CI 0.76-0.88; p < .001), laser trabeculoplasty (2,680 to 1,753, RR 0.79, CI 0.74-0.84; p < .001), and blepharoplasty (1,522 to 797, RR 0.71, CI 0.66-0.77; p < .001) all declined significantly. All procedures except laser iridotomy returned to pre-COVID19 rates by June 2020. CONCLUSION: Most ophthalmic procedures that significantly declined during the COVID-19 pandemic were elective procedures. Among these, the majority returned to 2019 daily averages by June 2020.

Impact of the COVID-19 Pandemic on Treatment Patterns for Patients With Metastatic Solid Cancer in the United States.

BACKGROUND: The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic's impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. METHODS: We used an electronic health record-derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. RESULTS: The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = -2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = -2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age. CONCLUSIONS: Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers.

Automated Text-Based Symptom Monitoring With Rapid Clinician Triage for Patients With Cancer and Suspected or Confirmed COVID-19.

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.

Association of Patient Characteristics With Delivery of Ophthalmic Telemedicine During the COVID-19 Pandemic.

Importance: Telemedicine has been shown to have had reduced uptake among historically marginalized populations within multiple medical specialties during the COVID-19 pandemic. An evaluation of health disparities among patients receiving ophthalmic telemedical care during the pandemic is needed. Objective: To evaluate disparities in the delivery of ophthalmic telemedicine at Massachusetts Eye and Ear (MEE) during the COVID-19 pandemic. Design, Setting, and Participants: This retrospective, cross-sectional study analyzed clinical visits at a single tertiary eye care center (MEE) from January 1 to December 31, 2020. Patients who had ophthalmology and optometry clinical visits at the MEE during the study period were included. Exposures: Telemedicine vs in-person clinical encounters. Main Outcomes and Measures: Variables associated with use of ophthalmic telemedicine during the study period. Results: A total of 2262 telemedicine ophthalmic encounters for 1911 patients were included in the analysis. The median age of the patients was 61 (interquartile range, 43-72) years, and 1179 (61.70%) were women. With regard to race and ethnicity, 87 patients (4.55%) identified as Asian; 128 (6.70%), as Black or African American; 23 (1.20%), as Hispanic or Latino; and 1455 (76.14%), as White. On multivariate analysis, factors associated with decreased receipt of telemedical care included male sex (odds ratio [OR], 0.86; 95% CI, 0.77-0.96), Black race (OR, 0.69; 95% CI, 0.56-0.86), not speaking English (OR, 0.63; 95% CI, 0.48-0.81), educational level of high school or less (OR, 0.83; 95% CI, 0.71-0.97), and age (OR per year of age, 0.99; 95% CI, 0.989-0.998). When comparing telephone- and video-based telemedicine visits, decreased participation in video-based visits was associated with age (OR per year of age, 0.96; 95% CI, 0.94-0.98), educational level of high school or less (OR, 0.54; 95% CI, 0.29-0.99), being unemployed (OR, 0.28; 95% CI, 0.12-0.68), being retired (OR, 0.22; 95% CI, 0.10-0.42), or having a disability (OR, 0.09; 95% CI, 0.04-0.23). Conclusions and Relevance: The findings of this cross-sectional study, though limited to retrospective data from a single university-based practice, suggest that historically marginalized populations were less likely to receive ophthalmic telemedical care compared with in-person care during the first year of the COVID-19 pandemic in the US. Understanding the causes of these disparities might help those who need access to virtual care.